Reduce Operational Costs with Custom AI Automation

Many businesses try to connect their apps using visual automation platforms. These are great for simple notifications, but they fail when a core business process depends on them. Their pricing models, based on a 'per-task' count, become expensive quickly. A workflow that reads an email, extracts an attachment, calls an AI model, and updates a CRM record can burn through 4 tasks for a single item.

A regional insurance agency with 6 adjusters faced this issue. They tried to automate claim intake from emails. The workflow needed to parse the PDF form, look up the policyholder in their CRM, and create a new record in their claims system. The platform's API connector would time out on policies with long histories, causing the entire workflow to fail silently. About 15% of claims never made it into the system, forcing a daily manual reconciliation that defeated the purpose of automation.

These platforms lack essential production features. There is no built-in logic for retrying a failed API call. There is no way to write a custom validation rule, like checking if a policy number matches a specific format. When it breaks, the business process stops, and there is no engineer to call because you are the one who dragged and dropped the boxes.

Syntora would begin an engagement by auditing your manual workflow and mapping it into a sequence of Python functions. We do not use visual editors; we write custom code. For document processing, a multi-step manual process can be defined as a state machine within a FastAPI application. We would use the Claude API for data extraction from documents, as we've built similar pipelines for financial documents, and its capabilities are well-suited for reliably parsing complex claim forms or applicant resumes.

The system would be engineered for resilience from its foundation. All external API calls, such as connecting to a CRM or an applicant tracking system, would use `httpx`, which incorporates automatic retries with exponential backoff. If an external API is temporarily unavailable, the system would automatically wait and retry the request multiple times before failing. We would use a lightweight database like Supabase to cache common lookups, improving performance for frequently accessed data.

The entire system would be deployed on AWS Lambda. This serverless function architecture allows the code to run efficiently in response to triggers, such as a new document arriving via email or an API call. This setup is highly efficient for event-driven workloads, often resulting in hosting costs under $50 per month for processing thousands of documents. All logs would be sent as structured JSON using `structlog` to AWS CloudWatch, enabling clear tracing of every step within a transaction.

Upon completion, Syntora would deliver the full source code in your company's private GitHub repository. The deliverable is a durable asset: a custom-built system designed to run on your infrastructure, using your API keys, and documented for any future engineer to maintain or extend. A typical engagement, from initial discovery to a deployed production system, usually spans 2 to 4 weeks, depending on complexity.